#   Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

import unittest

import gradient_checker
import numpy as np
from decorator_helper import prog_scope
from op_test import (
    OpTest,
    convert_float_to_uint16,
    get_device_place,
    get_places,
    is_custom_device,
    paddle_static_guard,
)

import paddle
from paddle import base
from paddle.base import core
from paddle.tensor.manipulation import tensor_array_to_tensor

paddle.enable_static()


def slice_wrapper(
    Input,
    axes=[],
    StartsTensor=None,
    EndsTensor=None,
    infer_flags=[],
    decrease_axis=[],
):
    return paddle._C_ops.slice(
        Input, axes, StartsTensor, EndsTensor, infer_flags, decrease_axis
    )


# Situation 1: starts(list, no tensor), ends(list, no tensor)
# 1.1 without attr(decrease)
class TestSliceOp(OpTest):
    def setUp(self):
        self.op_type = "slice"
        self.prim_op_type = "prim"
        self.python_api = paddle.slice
        self.public_python_api = paddle.slice
        self.config()
        self.inputs = {'Input': self.input}
        self.outputs = {'Out': self.out}
        self.attrs = {
            'axes': self.axes,
            'starts': self.starts,
            'ends': self.ends,
            'infer_flags': self.infer_flags,
        }

    def config(self):
        self.input = np.random.random([3, 4, 5, 6]).astype("float64")
        self.starts = [1, 0, 2]
        self.ends = [3, 3, 4]
        self.axes = [0, 1, 2]
        self.infer_flags = [1, 1, 1]
        self.out = self.input[1:3, 0:3, 2:4, :]

    def test_check_output(self):
        self.check_output(check_pir=True)

    def test_check_grad_normal(self):
        self.check_grad(
            ['Input'],
            'Out',
            max_relative_error=0.006,
            check_prim=True,
            check_pir=True,
            check_prim_pir=True,
        )


class TestCase1(TestSliceOp):
    def config(self):
        self.input = np.random.random([3, 4, 5, 6]).astype("float64")
        self.starts = [-3, 0, 2]
        self.ends = [3, 100, -1]
        self.axes = [0, 1, 2]
        self.infer_flags = [1, 1, 1]
        self.out = self.input[-3:3, 0:100, 2:-1, :]


class TestCase2(TestSliceOp):
    def config(self):
        self.input = np.random.random([3, 4, 5, 6]).astype("float64")
        self.starts = [-3, 0, 2]
        self.ends = [3, 100, -1]
        self.axes = [0, 1, 3]
        self.infer_flags = [1, 1, 1]
        self.out = self.input[-3:3, 0:100, :, 2:-1]


class TestCase3(TestSliceOp):
    def config(self):
        self.input = np.random.random([4, 4, 5, 6]).astype("float64")
        self.starts = [-3]
        self.ends = [3]
        self.axes = [0]
        self.infer_flags = [1, 1, 1]
        self.out = self.input[-3:3, :, :, :]


class TestCase4(TestSliceOp):
    def config(self):
        self.input = np.random.random([3, 4, 5, 6]).astype("float64")
        self.starts = [0]
        self.ends = [4]
        self.axes = [1]
        self.infer_flags = [1, 1, 1]
        self.out = self.input[:, :, :, :]


class TestCase5(TestSliceOp):
    def config(self):
        self.input = np.random.random([3, 4, 5, 6]).astype("float64")
        self.starts = [0]
        self.ends = [2]
        self.axes = [1]
        self.infer_flags = [1, 1, 1]
        self.out = self.input[:, 0:2, :, :]


class TestCase6(TestSliceOp):
    def config(self):
        self.input = np.random.random([3, 4, 5, 6]).astype("float64")
        self.starts = [2]
        self.ends = [4]
        self.axes = [1]
        self.infer_flags = [1, 1, 1]
        self.out = self.input[:, 2:4, :, :]


class TestSliceZerosShapeTensor(OpTest):
    def setUp(self):
        self.op_type = "slice"
        self.prim_op_type = "prim"
        self.python_api = paddle.slice
        self.public_python_api = paddle.slice
        self.config()
        self.inputs = {'Input': self.input}
        self.outputs = {'Out': self.out}
        self.attrs = {
            'axes': self.axes,
            'starts': self.starts,
            'ends': self.ends,
            'infer_flags': self.infer_flags,
            'use_onednn': True,
        }

    def config(self):
        self.input = np.random.random([0, 0, 0]).astype("float32")
        self.starts = [1]
        self.ends = [2]
        self.axes = [0]
        self.infer_flags = []
        self.out = self.input[1:2]

    def test_check_output(self):
        self.check_output_with_place(paddle.CPUPlace(), check_pir=True)


class TestCase_ZeroSize(TestSliceOp):
    def config(self):
        self.input = np.random.random([0, 0, 5, 6]).astype("float64")
        self.starts = [-3, 0, 2]
        self.ends = [3, 100, -1]
        self.axes = [0, 1, 3]
        self.infer_flags = [1, 1, 1]
        self.out = self.input[-3:3, 0:100, :, 2:-1]


# 1.2 with attr(decrease)
class TestSliceOp_decs_dim(OpTest):
    def setUp(self):
        self.enable_cinn = True
        self.op_type = "slice"
        self.prim_op_type = "prim"
        self.python_api = paddle.slice
        self.public_python_api = paddle.slice
        self.config()
        self.inputs = {'Input': self.input}
        self.outputs = {'Out': self.out}
        self.attrs = {
            'axes': self.axes,
            'starts': self.starts,
            'ends': self.ends,
            'infer_flags': self.infer_flags,
            'decrease_axis': self.decrease_axis,
        }

    def config(self):
        self.input = np.random.random([3, 4, 5, 6]).astype("float64")
        self.starts = [1, 0, 2]
        self.ends = [2, 3, 4]
        self.axes = [0, 1, 2]
        self.decrease_axis = []
        self.infer_flags = [1, 1, 1]
        self.out = self.input[1:2, 0:3, 2:4, :]

    def test_check_output(self):
        self.check_output(check_pir=True)

    def test_check_grad_normal(self):
        self.check_grad(
            ['Input'],
            'Out',
            max_relative_error=0.006,
            check_prim=True,
            check_pir=True,
            check_prim_pir=True,
        )


# without attr(decrease)
class TestSliceOp_starts_ListTensor(OpTest):
    def setUp(self):
        self.op_type = "slice"
        self.python_api = slice_wrapper
        self.config()

        starts_tensor = []
        for index, ele in enumerate(self.starts):
            starts_tensor.append(
                ("x" + str(index), np.ones(1).astype('int64') * ele)
            )

        self.inputs = {'Input': self.input, 'StartsTensorList': starts_tensor}
        self.outputs = {'Out': self.out}
        self.attrs = {
            'axes': self.axes,
            'starts': self.starts_infer,
            'ends': self.ends,
            'infer_flags': self.infer_flags,
        }

    def config(self):
        self.input = np.random.random([3, 4, 5, 6]).astype("float64")
        self.starts = [1, 0, 2]
        self.ends = [3, 3, 4]
        self.axes = [0, 1, 2]
        self.infer_flags = [-1, 1, -1]
        self.out = self.input[1:3, 0:3, 2:4, :]

        self.starts_infer = [-1, 0, -1]

    def test_check_output(self):
        self.check_output(check_pir=True, check_symbol_infer=False)

    def test_check_grad_normal(self):
        self.check_grad(
            ['Input'], 'Out', max_relative_error=0.006, check_pir=True
        )


# Situation 2: starts(list, have tensor), ends(list, no tensor)
#  with attr(decrease)
class TestSliceOp_decs_dim_starts_ListTensor(OpTest):
    def setUp(self):
        self.op_type = "slice"
        self.python_api = slice_wrapper
        self.config()

        starts_tensor = []
        for index, ele in enumerate(self.starts):
            starts_tensor.append(
                ("x" + str(index), np.ones(1).astype('int32') * ele)
            )

        self.inputs = {'Input': self.input, 'StartsTensorList': starts_tensor}

        self.outputs = {'Out': self.out}
        self.attrs = {
            'axes': self.axes,
            'starts': self.starts_infer,
            'ends': self.ends,
            'infer_flags': self.infer_flags,
            'decrease_axis': self.decrease_axis,
        }

    def config(self):
        self.input = np.random.random([3, 4, 5, 6]).astype("float64")
        self.starts = [1, 0, 2]
        self.ends = [2, 3, 4]
        self.axes = [0, 1, 2]
        self.decrease_axis = [0]
        self.infer_flags = [1, -1, 1]
        self.out = self.input[1, 0:3, 2:4, :]

        self.starts_infer = [1, -1, 2]

    def test_check_output(self):
        self.check_output(
            check_dygraph=True, check_pir=True, check_symbol_infer=False
        )

    def test_check_grad_normal(self):
        self.check_grad(
            ['Input'], 'Out', max_relative_error=0.006, check_pir=True
        )


class TestSliceOp_decs_dim_5_starts_ListTensor(
    TestSliceOp_decs_dim_starts_ListTensor
):
    def config(self):
        self.input = np.random.random([3, 4, 5, 6]).astype("float64")
        self.starts = [-1]
        self.ends = [1000000]
        self.axes = [3]
        self.decrease_axis = [3]
        self.infer_flags = [-1]
        self.out = self.input[:, :, :, -1]

        self.starts_infer = [-1]


# Situation 3: starts(tensor), ends(list, no tensor)
# with attr(decrease)
class TestSliceOp_decs_dim_starts_OneTensor(OpTest):
    def setUp(self):
        self.op_type = "slice"
        self.python_api = slice_wrapper
        self.config()
        self.inputs = {
            'Input': self.input,
            "StartsTensor": np.array(self.starts, dtype="int32"),
        }
        self.outputs = {'Out': self.out}
        self.attrs = {
            'axes': self.axes,
            # 'starts': self.starts,
            'ends': self.ends,
            'infer_flags': self.infer_flags,
            'decrease_axis': self.decrease_axis,
        }

    def config(self):
        self.input = np.random.random([3, 4, 5, 6]).astype("float64")
        self.starts = [1, 0, 2]
        self.ends = [2, 3, 4]
        self.axes = [0, 1, 2]
        self.decrease_axis = [0]
        self.infer_flags = [-1, -1, -1]
        self.out = self.input[1, 0:3, 2:4, :]

    def test_check_output(self):
        self.check_output(check_pir=True, check_symbol_infer=False)

    def test_check_grad_normal(self):
        self.check_grad(
            ['Input'], 'Out', max_relative_error=0.006, check_pir=True
        )


# Situation 4: starts(tensor), ends(tensor)
#  without attr(decrease)
class TestSliceOp_starts_OneTensor_ends_OneTensor(OpTest):
    def setUp(self):
        self.op_type = "slice"
        self.python_api = slice_wrapper
        self.config()

        self.inputs = {
            'Input': self.input,
            "StartsTensor": np.array(self.starts, dtype="int64"),
            "EndsTensor": np.array(self.ends, dtype="int32"),
        }
        self.outputs = {'Out': self.out}
        self.attrs = {
            'axes': self.axes,
            # 'starts': self.starts,
            # 'ends': self.ends_infer,
            'infer_flags': self.infer_flags,
        }

    def config(self):
        self.input = np.random.random([3, 4, 5, 6]).astype("float64")
        self.starts = [1, 0, 2]
        self.ends = [3, 3, 4]
        self.axes = [0, 1, 2]
        self.infer_flags = [-1, -1, -1]
        self.out = self.input[1:3, 0:3, 2:4, :]

    def test_check_output(self):
        self.check_output(check_pir=True, check_symbol_infer=False)

    def test_check_grad_normal(self):
        self.check_grad(
            ['Input'], 'Out', max_relative_error=0.006, check_pir=True
        )


# Situation 5: starts(tensor), ends(tensor)
#  with attr(decrease)
class TestSliceOp_decs_dim_starts_and_ends_OneTensor(OpTest):
    def setUp(self):
        self.op_type = "slice"
        self.python_api = slice_wrapper
        self.config()
        self.inputs = {
            'Input': self.input,
            "StartsTensor": np.array(self.starts, dtype="int32"),
            "EndsTensor": np.array(self.ends, dtype="int32"),
        }
        self.outputs = {'Out': self.out}
        self.attrs = {
            'axes': self.axes,
            # 'starts': self.starts,
            # 'ends': self.ends,
            'infer_flags': self.infer_flags,
            'decrease_axis': self.decrease_axis,
        }

    def config(self):
        self.input = np.random.random([3, 4, 5, 6]).astype("float64")
        self.starts = [1, 0, 2]
        self.ends = [2, 1, 4]
        self.axes = [0, 1, 2]
        self.decrease_axis = [0, 1]
        self.infer_flags = [-1, -1, -1]
        self.out = self.input[1, 0, 2:4, :]

    def test_check_output(self):
        self.check_output(check_pir=True, check_symbol_infer=False)

    def test_check_grad_normal(self):
        self.check_grad(
            ['Input'], 'Out', max_relative_error=0.006, check_pir=True
        )


# Situation 6: starts(tensor), ends(list, have tensor)
# without attr(decrease)
class TestSliceOp_starts_OneTensor_ends_ListTensor(OpTest):
    def setUp(self):
        self.op_type = "slice"
        self.python_api = slice_wrapper
        self.config()

        ends_tensor = []
        for index, ele in enumerate(self.ends):
            ends_tensor.append(
                ("y" + str(index), np.ones(1).astype('int32') * ele)
            )

        self.inputs = {
            'Input': self.input,
            "StartsTensor": np.array(self.starts, dtype="int32"),
            'EndsTensorList': ends_tensor,
        }
        self.outputs = {'Out': self.out}
        self.attrs = {
            'axes': self.axes,
            # 'starts': self.starts,
            'ends': self.ends_infer,
            'infer_flags': self.infer_flags,
        }

    def config(self):
        self.input = np.random.random([3, 4, 5, 6]).astype("float64")
        self.starts = [1, 0, 2]
        self.ends = [3, 3, 4]
        self.axes = [0, 1, 2]
        self.infer_flags = [-1, -1, -1]
        self.out = self.input[1:3, 0:3, 2:4, :]

        self.ends_infer = [-1, 3, 4]

    def test_check_output(self):
        self.check_output(check_pir=True, check_symbol_infer=False)

    def test_check_grad_normal(self):
        self.check_grad(
            ['Input'], 'Out', max_relative_error=0.006, check_pir=True
        )


class TestSliceOp_ZeroDim(OpTest):
    def setUp(self):
        self.op_type = "slice"
        self.python_api = slice_wrapper
        self.config()

        starts_tensor = []
        ends_tensor = []

        for index, ele in enumerate(self.starts):
            starts_tensor.append(
                ("x" + str(index), np.array(1).astype('int32'))
            )

        for index, ele in enumerate(self.ends):
            ends_tensor.append(("y" + str(index), np.array(3).astype('int32')))
        self.inputs = {
            'Input': self.input,
            "StartsTensorList": starts_tensor,
            'EndsTensorList': ends_tensor,
        }
        self.outputs = {'Out': self.out}
        self.attrs = {
            'axes': self.axes,
            'infer_flags': self.infer_flags,
        }

    def config(self):
        self.input = np.random.random([20, 3, 3]).astype("float64")
        self.starts = [1, 1]
        self.ends = [3, 3]
        self.axes = [1, 2]
        self.infer_flags = [-1, -1]
        self.out = self.input[0:20, 1:3, 1:3]

    def test_check_output(self):
        self.check_output(check_pir=True, check_symbol_infer=False)

    def test_check_grad_normal(self):
        self.check_grad(['Input'], 'Out', check_pir=True)


# Test CUDA float16
@unittest.skipIf(
    not (core.is_compiled_with_cuda() or is_custom_device()),
    "core is not compiled with CUDA",
)
class TestFP16(OpTest):
    def setUp(self):
        self.op_type = "slice"
        self.prim_op_type = "prim"
        self.python_api = paddle.slice
        self.public_python_api = paddle.slice
        self.config()
        self.inputs = {'Input': self.input}
        self.outputs = {'Out': self.out}
        self.attrs = {
            'axes': self.axes,
            'starts': self.starts,
            'ends': self.ends,
            'infer_flags': self.infer_flags,
        }

    def config(self):
        self.dtype = "float16"
        self.input = np.random.random([3, 4, 5, 6]).astype(self.dtype)
        self.starts = [-3, 0, 2]
        self.ends = [3, 100, -1]
        self.axes = [0, 1, 3]
        self.out = self.input[-3:3, 0:100, :, 2:-1]
        self.infer_flags = [1, 1, 1]

    def test_check_output(self):
        place = get_device_place()
        if core.is_float16_supported(place):
            self.check_output_with_place(
                place, check_prim=True, check_pir=True, check_prim_pir=True
            )

    def test_check_grad_normal(self):
        place = get_device_place()
        print("core:", core.is_float16_supported(place))
        if core.is_float16_supported(place):
            self.check_grad_with_place(
                place,
                ['Input'],
                'Out',
                check_prim=True,
                check_pir=True,
                check_prim_pir=True,
            )


@unittest.skipIf(
    not (core.is_compiled_with_cuda() or is_custom_device()),
    "core is not compiled with CUDA",
)
class TestFP16_2(OpTest):
    def setUp(self):
        self.op_type = "slice"
        self.prim_op_type = "prim"
        self.python_api = paddle.slice
        self.public_python_api = paddle.slice
        self.config()
        self.inputs = {'Input': self.input}
        self.outputs = {'Out': self.out}
        self.attrs = {
            'axes': self.axes,
            'starts': self.starts,
            'ends': self.ends,
            'infer_flags': self.infer_flags,
        }

    def config(self):
        self.dtype = "float16"
        self.input = np.random.random([3, 4, 10]).astype(self.dtype)
        self.starts = [0]
        self.ends = [1]
        self.axes = [1]
        self.out = self.input[:, 0:1, :]
        self.infer_flags = [1]

    def test_check_output(self):
        place = get_device_place()
        if core.is_float16_supported(place):
            self.check_output_with_place(
                place, check_prim=True, check_pir=True, check_prim_pir=True
            )

    def test_check_grad_normal(self):
        place = get_device_place()
        if core.is_float16_supported(place):
            self.check_grad_with_place(
                place,
                ['Input'],
                'Out',
                numeric_grad_delta=0.5,
                check_prim=True,
                check_pir=True,
                check_prim_pir=True,
            )


class TestBF16(OpTest):
    def setUp(self):
        self.op_type = "slice"
        self.prim_op_type = "prim"
        self.python_api = paddle.slice
        self.public_python_api = paddle.slice
        self.config()
        self.inputs = {'Input': convert_float_to_uint16(self.input)}
        self.outputs = {'Out': convert_float_to_uint16(self.out)}
        self.attrs = {
            'axes': self.axes,
            'starts': self.starts,
            'ends': self.ends,
            'infer_flags': self.infer_flags,
        }

    def config(self):
        self.dtype = np.uint16
        self.input = np.random.random([3, 4, 5, 6]).astype(np.float32)
        self.starts = [-3, 0, 2]
        self.ends = [3, 100, -1]
        self.axes = [0, 1, 3]
        self.out = self.input[-3:3, 0:100, :, 2:-1]
        self.infer_flags = [1, 1, 1]

    def test_check_output(self):
        self.check_output(check_pir=True)

    def test_check_grad_normal(self):
        self.check_grad(
            ['Input'],
            'Out',
            check_prim=True,
            check_pir=True,
            check_prim_pir=True,
        )


# Test python API
class TestSliceAPI(unittest.TestCase):
    def test_1(self):
        with paddle_static_guard():
            input = np.random.random([3, 4, 5, 6]).astype("float64")
            minus_1 = paddle.tensor.fill_constant([], "int32", -1)
            minus_3 = paddle.tensor.fill_constant([], "int64", -3)
            starts = paddle.static.data(
                name='starts', shape=[1, 3], dtype="float32"
            )
            if not paddle.framework.use_pir_api():
                starts.desc.set_need_check_feed(False)
            ends = paddle.static.data(name='ends', shape=[3], dtype="float32")
            if not paddle.framework.use_pir_api():
                ends.desc.set_need_check_feed(False)
            x = paddle.static.data(
                name="x",
                shape=[3, 4, 5, 6],
                dtype="float64",
            )

            # value_int64 is greater than 2147483647 which is the max of int32
            value_int64 = paddle.tensor.fill_constant([1], "int64", 2147483648)

            out_1 = paddle.slice(
                x,
                axes=[0, 1, 2],
                starts=[-3, 0, 2],
                ends=[value_int64, 100, -1],
            )
            out_2 = paddle.slice(
                x, axes=[0, 1, 3], starts=[minus_3, 0, 2], ends=[3, 100, -1]
            )
            out_3 = paddle.slice(
                x,
                axes=[0, 1, 3],
                starts=[minus_3, 0, 2],
                ends=[3, 100, minus_1],
            )
            out_4 = paddle.slice(x, axes=[0, 1, 2], starts=starts, ends=ends)

            out_5 = x[-3:3, 0:100, 2:-1]
            out_6 = x[minus_3:3, 0:100, :, 2:-1]
            out_7 = x[minus_1, 0:100, :, 2:minus_1]

            exe = base.Executor(place=base.CPUPlace())
            res_1, res_2, res_3, res_4, res_5, res_6, res_7 = exe.run(
                paddle.static.default_main_program(),
                feed={
                    "x": input,
                    'starts': np.array([-3, 0, 2]).astype("int32"),
                    'ends': np.array([3, 100, -1]).astype("int32"),
                },
                fetch_list=[
                    out_1,
                    out_2,
                    out_3,
                    out_4,
                    out_5,
                    out_6,
                    out_7,
                ],
            )

            np.testing.assert_array_equal(res_1, input[-3:3, 0:100, 2:-1, :])
            np.testing.assert_array_equal(res_2, input[-3:3, 0:100, :, 2:-1])
            np.testing.assert_array_equal(res_3, input[-3:3, 0:100, :, 2:-1])
            np.testing.assert_array_equal(res_4, input[-3:3, 0:100, 2:-1, :])
            np.testing.assert_array_equal(res_5, input[-3:3, 0:100, 2:-1, :])
            np.testing.assert_array_equal(res_6, input[-3:3, 0:100, :, 2:-1])
            np.testing.assert_array_equal(res_7, input[-1, 0:100, :, 2:-1])

    def test_pir(self):
        with (
            paddle.pir_utils.IrGuard(),
            paddle.static.program_guard(paddle.static.Program()),
        ):
            input = np.random.random([3, 4, 5, 6]).astype("float64")
            minus_1 = paddle.tensor.fill_constant([], "int32", -1)
            minus_3 = paddle.tensor.fill_constant([], "int64", -3)
            starts = paddle.static.data(name='starts', shape=[3], dtype="int32")
            ends = paddle.static.data(name='ends', shape=[3], dtype="int32")
            x = paddle.static.data(
                name="x",
                shape=[3, 4, 5, 6],
                dtype="float64",
            )

            # value_int64 is greater than 2147483647 which is the max of int32
            value_int64 = paddle.tensor.fill_constant([1], "int64", 2147483648)

            out_1 = paddle.slice(
                x,
                axes=[0, 1, 2],
                starts=[-3, 0, 2],
                ends=[value_int64, 100, -1],
            )
            out_2 = paddle.slice(
                x, axes=[0, 1, 3], starts=[minus_3, 0, 2], ends=[3, 100, -1]
            )
            out_3 = paddle.slice(
                x,
                axes=[0, 1, 3],
                starts=[minus_3, 0, 2],
                ends=[3, 100, minus_1],
            )
            out_4 = paddle.slice(x, axes=[0, 1, 2], starts=starts, ends=ends)

            out_5 = x[-3:3, 0:100, 2:-1]
            out_6 = x[minus_3:3, 0:100, :, 2:-1]
            # open it after supporting control flow
            # out_7 = x[minus_1, 0:100, :, 2:minus_1]

            exe = base.Executor(place=base.CPUPlace())
            res_1, res_2, res_3, res_4, res_5, res_6 = exe.run(
                paddle.static.default_main_program(),
                feed={
                    "x": input,
                    'starts': np.array([-3, 0, 2]).astype("int32"),
                    'ends': np.array([3, 100, -1]).astype("int32"),
                },
                fetch_list=[out_1, out_2, out_3, out_4, out_5, out_6],
            )

            np.testing.assert_array_equal(res_1, input[-3:3, 0:100, 2:-1, :])
            np.testing.assert_array_equal(res_2, input[-3:3, 0:100, :, 2:-1])
            np.testing.assert_array_equal(res_3, input[-3:3, 0:100, :, 2:-1])
            np.testing.assert_array_equal(res_4, input[-3:3, 0:100, 2:-1, :])
            np.testing.assert_array_equal(res_5, input[-3:3, 0:100, 2:-1, :])
            np.testing.assert_array_equal(res_6, input[-3:3, 0:100, :, 2:-1])
            # np.testing.assert_array_equal(res_7, input[-1, 0:100, :, 2:-1])

    # Test negative axis
    def test_negative_axis_dygraph(self):
        with paddle.base.dygraph.guard():
            input = np.random.random([3, 4, 5, 6]).astype("float64")

            res = paddle.slice(
                paddle.to_tensor(input), axes=[-2], starts=[2], ends=[3]
            )
            np.testing.assert_array_equal(res, input[:, :, 2:3, :])

    def test_negative_axis_static(self):
        with (
            paddle_static_guard(),
            paddle.static.program_guard(paddle.static.Program()),
        ):
            input = np.random.random([3, 4, 5, 6]).astype("float64")
            x = paddle.static.data(
                name="x",
                shape=[3, 4, 5, 6],
                dtype="float64",
            )

            out = paddle.slice(
                x,
                axes=[-2],
                starts=[2],
                ends=[3],
            )

            exe = base.Executor(place=base.CPUPlace())
            res = exe.run(
                feed={
                    "x": input,
                },
                fetch_list=[out],
            )[0]

            np.testing.assert_array_equal(res, input[:, :, 2:3, :])

    def test_negative_axis_pir(self):
        with (
            paddle.pir_utils.IrGuard(),
            paddle.static.program_guard(paddle.static.Program()),
        ):
            input = np.random.random([3, 4, 5, 6]).astype("float64")
            x = paddle.static.data(
                name="x",
                shape=[3, 4, 5, 6],
                dtype="float64",
            )

            out = paddle.slice(
                x,
                axes=[-2],
                starts=[2],
                ends=[3],
            )

            exe = base.Executor(place=base.CPUPlace())
            res = exe.run(
                paddle.static.default_main_program(),
                feed={
                    "x": input,
                },
                fetch_list=[out],
            )[0]

            np.testing.assert_array_equal(res, input[:, :, 2:3, :])


class TestSliceApiWithTensor(unittest.TestCase):
    def test_starts_ends_is_tensor(self):
        with paddle.base.dygraph.guard():
            a = paddle.rand(shape=[4, 5, 6], dtype='float32')
            axes = [0, 1, 2]
            starts = [-3, 0, 2]
            ends = [3, 2, 4]
            a_1 = paddle.slice(
                a,
                axes=axes,
                starts=paddle.to_tensor(starts, dtype='int32'),
                ends=paddle.to_tensor(ends, dtype='int32'),
            )
            a_2 = paddle.slice(a, axes=axes, starts=starts, ends=ends)

            np.testing.assert_array_equal(a_1.numpy(), a_2.numpy())

    def test_bool_tensor(self):
        with paddle.base.dygraph.guard():
            array = (np.arange(60).reshape([3, 4, 5]) % 3).astype('bool')
            tt = paddle.to_tensor(array)
            tt.stop_gradient = False

            starts = [0, 1, 2]
            ends = [3, 5, 4]
            axes = [0, 1, 2]

            y_paddle = paddle.slice(tt, axes, starts, ends)
            y_np = tt[0:3, 1:5, 2:4]

            self.assertTrue(paddle.bool == y_paddle.dtype)
            np.testing.assert_array_equal(y_paddle.numpy(), y_np)


class TestSliceApiEager(unittest.TestCase):
    def test_slice_api(self):
        with paddle.base.dygraph.guard():
            a = paddle.rand(shape=[4, 5, 6], dtype='float32')
            a.stop_gradient = False
            axes = [0, 1, 2]
            starts = [-3, 0, 2]
            ends = [3, 2, 4]
            a_1 = paddle.slice(a, axes=axes, starts=starts, ends=ends)

            a_2 = paddle.slice(
                a,
                axes=axes,
                starts=paddle.to_tensor(starts),
                ends=paddle.to_tensor(ends),
            )
            np.testing.assert_array_equal(a_1.numpy(), a_2.numpy())
            a_1.backward()
            grad_truth = paddle.zeros_like(a)
            grad_truth[-3:3, 0:2, 2:4] = 1
            np.testing.assert_array_equal(grad_truth, a.gradient())

            np.testing.assert_allclose(
                a_1.numpy(), a[-3:3, 0:2, 2:4], rtol=1e-05
            )


class TestSliceApiWithDenseTensorArray(unittest.TestCase):
    def setUp(self):
        self.shape = (3, 4)
        self.data = np.random.random(size=self.shape).astype('float32')
        self.idx = 0
        self.start = 0
        self.end = 2
        self.axis = 1

        self.place = get_device_place()
        self.exe = base.Executor(self.place)

    def set_program_and_run(self, main_program, case_num):
        with (
            paddle.pir_utils.OldIrGuard(),
            paddle_static_guard(),
            paddle.static.program_guard(main_program),
        ):
            x = [
                paddle.static.data(
                    name='x0', shape=self.shape, dtype="float32"
                ),
                paddle.static.data(
                    name='x1', shape=self.shape, dtype="float32"
                ),
                paddle.static.data(
                    name='x2', shape=self.shape, dtype="float32"
                ),
            ]

            for each_x in x:
                each_x.stop_gradient = False

            arr = paddle.tensor.create_array(dtype="float32")
            for i in range(3):
                idx = paddle.tensor.array_length(arr)
                arr = paddle.tensor.array_write(x=x[i], i=idx, array=arr)

            if case_num == 1:
                self.sliced_arr = output = arr[0]

            elif case_num == 2:
                end = (
                    paddle.tensor.array_length(arr) - 1
                )  # dtype of end is int64
                self.sliced_arr = slice_arr = arr[self.start : end]
                output, _ = tensor_array_to_tensor(
                    slice_arr, axis=self.axis, use_stack=True
                )
            elif case_num == 3:
                value_int64 = paddle.tensor.fill_constant(
                    [1], "int64", 2147483648
                )
                self.sliced_arr = slice_arr = arr[self.start : value_int64]
                output, _ = tensor_array_to_tensor(
                    slice_arr, axis=self.axis, use_stack=True
                )

            loss = paddle.sum(output)
            base.backward.append_backward(loss)
            g_vars = list(
                map(
                    main_program.global_block().var,
                    [each_x.name + "@GRAD" for each_x in x],
                )
            )
            self.out, self.g_x0, self.g_x1, self.g_x2 = self.exe.run(
                main_program,
                feed={
                    'x0': self.data,
                    'x1': self.data,
                    'x2': self.data,
                },
                fetch_list=[output, *g_vars],
            )

        def test_case_1(self):
            main_program = paddle.static.Program()
            self.set_program_and_run(main_program, 1)

            self.assertTrue(
                self.sliced_arr.type == core.VarDesc.VarType.DENSE_TENSOR
            )
            self.assertEqual(self.sliced_arr.shape, self.shape)
            np.testing.assert_array_equal(self.out, self.data)
            np.testing.assert_array_equal(self.g_x0, np.ones_like(self.data))
            np.testing.assert_array_equal(self.g_x1, np.zeros_like(self.data))
            np.testing.assert_array_equal(self.g_x2, np.zeros_like(self.data))

        def test_case_2(self):
            with paddle_static_guard():
                main_program = paddle.static.Program()
                self.set_program_and_run(main_program, 2)

                self.assertTrue(
                    self.sliced_arr.type
                    == core.VarDesc.VarType.DENSE_TENSOR_ARRAY
                )
                self.assertEqual(self.sliced_arr.shape, self.shape)
                np.testing.assert_array_equal(
                    self.out, np.stack([self.data, self.data], axis=self.axis)
                )
                np.testing.assert_array_equal(
                    self.g_x0, np.ones_like(self.data)
                )
                np.testing.assert_array_equal(
                    self.g_x1, np.ones_like(self.data)
                )
                np.testing.assert_array_equal(
                    self.g_x2, np.zeros_like(self.data)
                )

        def test_case_3(self):
            with paddle_static_guard():
                main_program = paddle.static.Program()
                self.set_program_and_run(main_program, 3)

                self.assertTrue(
                    self.sliced_arr.type
                    == core.VarDesc.VarType.DENSE_TENSOR_ARRAY
                )
                self.assertEqual(self.sliced_arr.shape, self.shape)
                np.testing.assert_array_equal(
                    self.out,
                    np.stack([self.data, self.data, self.data], axis=self.axis),
                )
                np.testing.assert_array_equal(
                    self.g_x0, np.ones_like(self.data)
                )
                np.testing.assert_array_equal(
                    self.g_x1, np.ones_like(self.data)
                )
                np.testing.assert_array_equal(
                    self.g_x2, np.ones_like(self.data)
                )

    class TestImperativeVarBaseGetItem(unittest.TestCase):
        def test_getitem_with_long(self):
            with base.dygraph.guard():
                data = np.random.random((2, 80, 16128)).astype('float32')
                var = paddle.to_tensor(data)
                sliced = var[:, 10:, : var.shape[1]]  # var.shape[1] is 80L here
                self.assertEqual(sliced.shape, [2, 70, 80])

                sliced = var[:, var.shape[0] :, var.shape[0] : var.shape[1]]
                self.assertEqual(sliced.shape, [2, 78, 78])

        def test_getitem_with_float(self):
            def test_float_in_slice_item():
                with base.dygraph.guard():
                    data = np.random.random((2, 80, 16128)).astype('float32')
                    var = paddle.to_tensor(data)
                    sliced = var[:, 1.1:, : var.shape[1]]

            self.assertRaisesRegex(
                ValueError,
                r"\(InvalidArgument\) Currently, slice indices only allows None",
                test_float_in_slice_item,
            )

            def test_float_in_index():
                with base.dygraph.guard():
                    data = np.random.random((2, 80, 16128)).astype('float32')
                    var = paddle.to_tensor(data)
                    sliced = var[1.1]

            self.assertRaisesRegex(
                ValueError,
                r"\(InvalidArgument\) Currently, Tensor.__indices__\(\) only allows indexing by Boolean",
                test_float_in_index,
            )

    class TestInferShape(unittest.TestCase):
        def test_pir(self):
            with paddle.pir_utils.IrGuard():
                x = paddle.static.data('x', shape=[3, -1, 5])

                out0 = paddle.slice(x, axes=[1], starts=[0], ends=[3])
                self.assertEqual(out0.shape, [3, -1, 5])

        def test_axis_less_than_zero(self):
            # Using paddle.disable_static will make other unittests fail.
            with base.dygraph.guard():
                x_arr = np.arange(0, 24, dtype=np.float32).reshape([2, 3, 4])
                x = paddle.to_tensor(x_arr)

                pp_slice = paddle.slice(
                    x,
                    [
                        100,
                    ],
                    [0],
                    [1],
                )
                np_slice = x_arr[:, :, 0:1]
                np.testing.assert_array_equal(pp_slice, np_slice)

                pp_slice = paddle.slice(x, (-100,), [0], [1])
                np_slice = x_arr[0:1]
                np.testing.assert_array_equal(pp_slice, np_slice)

                x_arr = np.array([], dtype=np.float32)
                x = paddle.to_tensor(np.reshape(x_arr, (0, 0, 0)))

                starts = paddle.to_tensor(
                    np.reshape(np.array([], dtype=np.int32), (0,))
                )
                ends = paddle.to_tensor(
                    np.reshape(np.array([], dtype=np.int32), (0,))
                )

                with self.assertRaises(ValueError):
                    paddle.slice(x, [-1000000], starts, ends)

                with self.assertRaises(ValueError):
                    paddle.slice(x, [1000000], starts, ends)

                with self.assertRaises(ValueError):
                    paddle.slice(x, [], starts, ends)

                with self.assertRaises(ValueError):
                    paddle.slice(x, 0, starts, ends)


class TestSliceOpError(unittest.TestCase):
    def test_mismatch_shape(self):
        with base.dygraph.guard():
            with self.assertRaises(ValueError):
                array = np.array([], dtype=np.float32)
                x = paddle.to_tensor(np.reshape(array, [0]), dtype='float32')
                paddle.slice(x, axes=[0], starts=[], ends=[])

            with self.assertRaises(ValueError):
                array = np.array([], dtype=np.float32)
                x = paddle.to_tensor(np.reshape(array, [0]), dtype='float32')
                paddle.slice(x, axes=[0], starts=[0], ends=[])

            # if shape match, pass
            array = np.array([], dtype=np.float32)
            x = paddle.to_tensor(np.reshape(array, [0]), dtype='float32')
            out = paddle.slice(x, axes=[0], starts=[0], ends=[0])
            self.assertEqual(out.numel(), 0)
            # self.assertEqual(out.shape)


@unittest.skipIf(
    not (core.is_compiled_with_cuda()),
    "core is not compiled with CUDA",
)
class TestImperativeCUDAPinnedInput(unittest.TestCase):
    def test_input_cuda_pinned_var(self):
        with base.dygraph.guard():
            data = np.random.random((2, 80, 16128)).astype('float32')
            var = core.eager.Tensor(
                value=data,
                name='',
                persistable=False,
                place=base.CUDAPinnedPlace(),
                zero_copy=False,
            )
            sliced = var[:, 10:, : var.shape[1]]
            self.assertEqual(sliced.shape, [2, 70, 80])


class TestSliceDoubleGradCheck(unittest.TestCase):
    def slice_wrapper(self, x):
        return paddle.slice(
            x[0], axes=[0, 1, 2], starts=[-3, 0, 2], ends=[3, 2, 4]
        )

    @prog_scope()
    def func(self, place):
        # the shape of input variable should be clearly specified, not include -1.
        eps = 0.005
        dtype = np.float32

        data = paddle.static.data('data', [4, 5, 6], dtype)
        data.persistable = True
        out = paddle.slice(
            data, axes=[0, 1, 2], starts=[-3, 0, 2], ends=[3, 2, 4]
        )
        data_arr = np.random.uniform(-1, 1, data.shape).astype(dtype)

        gradient_checker.double_grad_check(
            [data], out, x_init=[data_arr], place=place, eps=eps
        )
        gradient_checker.double_grad_check_for_dygraph(
            self.slice_wrapper, [data], out, x_init=[data_arr], place=place
        )

    def test_grad(self):
        with paddle_static_guard():
            for p in get_places():
                self.func(p)


class TestSliceTripleGradCheck(unittest.TestCase):
    def slice_wrapper(self, x):
        return paddle.slice(
            x[0], axes=[0, 1, 2], starts=[-3, 0, 2], ends=[3, 2, 4]
        )

    @prog_scope()
    def func(self, place):
        # the shape of input variable should be clearly specified, not include -1.
        eps = 0.005
        dtype = np.float32

        data = paddle.static.data('data', [4, 5, 6], dtype)
        data.persistable = True
        out = paddle.slice(
            data, axes=[0, 1, 2], starts=[-3, 0, 2], ends=[3, 2, 4]
        )
        data_arr = np.random.uniform(-1, 1, data.shape).astype(dtype)

        gradient_checker.triple_grad_check(
            [data], out, x_init=[data_arr], place=place, eps=eps
        )
        gradient_checker.triple_grad_check_for_dygraph(
            self.slice_wrapper, [data], out, x_init=[data_arr], place=place
        )

    def test_grad(self):
        with paddle_static_guard():
            for p in get_places():
                self.func(p)


class TestSliceTensorArray(unittest.TestCase):
    def test_slice_range(self):
        with paddle.pir_utils.IrGuard():
            arr = paddle.tensor.create_array("int32")
            x = paddle.static.data("x", shape=[2, 2], dtype="int32")
            y = paddle.static.data("y", shape=[1, 2], dtype="int32")

            zero = paddle.tensor.creation.fill_constant([], 'int64', 0)
            paddle.tensor.array_write(x, zero, array=arr)
            paddle.tensor.array_write(y, zero + 1, array=arr)

            sliced_array = paddle._pir_ops.slice_array(arr, [0], [1])
            self.assertTrue(sliced_array.is_dense_tensor_array_type())
            self.assertEqual(sliced_array.dtype, paddle.pir.core.DataType.INT32)

    def test_slice_item(self):
        with paddle.pir_utils.IrGuard():
            arr = paddle.tensor.create_array("int32")
            x = paddle.static.data("x", shape=[2, 2], dtype="int32")
            y = paddle.static.data("y", shape=[1, 2], dtype="int32")

            zero = paddle.tensor.creation.fill_constant([], 'int64', 0)
            paddle.tensor.array_write(x, zero, array=arr)
            paddle.tensor.array_write(y, zero + 1, array=arr)

            sliced_item = paddle._pir_ops.slice_array_dense(arr, [0])
            self.assertTrue(sliced_item.is_dense_tensor_type())
            self.assertEqual(sliced_item.dtype, paddle.pir.core.DataType.INT32)
            # TODO(dev): sliced item shape should be [-1, 2]


if __name__ == '__main__':
    paddle.enable_static()
    unittest.main()
